摘要
海上风机齿轮箱结构复杂、故障多发,同时受海上风机运行的强噪声干扰,轴承故障的特征信号提取较为困难。针对以上问题,提出了一种基于最大相关峭度解卷积(MCKD)的故障诊断方法,通过MCKD算法对振动信号进行降噪处理和特征增强,并利用增强包络谱对轴承的故障特征频率进行提取,从而实现对轴承的故障诊断。将该方法应用到海上风机齿轮箱轴承的模拟信号和实测信号中,研究结果表明:该方法对海上强噪声环境下齿轮箱轴承故障的特征提取和诊断具有良好的效果。
The gearbox of offshore wind turbines features a complex structure that is susceptible to faults.Additionally,the characteristic signals of bearing faults are challenging to extract due to significant noise interference during wind turbine operation.To tackle these challenges,a fault diagnosis method based on Maximum Correlation Kurtosis Deconvolution(MCKD)is proposed.The MCKD algorithm is used to denoise and enhance the feature of the vibration signal,and the enhanced envelope spectrum is used to extract the fault characteristic frequency of the bearing,so as to realize the fault diagnosis of the bearing.The method is applied to the analog signal and the measured signal of the gearbox bearing of the offshore wind turbine.The results show that the method has a good effect on the feature extraction and diagnosis of the gearbox bearing fault in a strong noise environment.
作者
郭奇
祁雷
赵杨
徐晴晴
刘浩
GUO Qi;QI Lei;ZHAO Yang;XU Qingqing;LIU Hao(Clean Energy Branch,CNOOC Energy Development Co.,Ltd.;College of Safety and Ocean Engineering,China University of Petroleum(Beijing))
出处
《油气田地面工程》
2024年第6期62-67,72,共7页
Oil-Gas Field Surface Engineering
基金
中海油能源发展股份有限公司-中国石油大学(北京)联合创新基金:海上风电工程技术规范与标准体系及关键共性技术研究(GD2021ZCAF0021)
中国石油科技创新基金研究项目:基于大数据的油气田站场风险预警技术(2021DQ02-0801)。
关键词
海上风机齿轮箱
轴承
故障诊断
最大相关峭度解卷积
增强包络谱
offshore wind turbine gearbox
bearings
fault diagnosis
maximum correlated kurtosis deconvolution
enhanced envelope spectrum
